High-performance computing has quickly become an integral part of scientific advancement, and a great deal of work has gone into making deep learning software easier to both create and deploy; however, many scientific projects have been bottlenecked by hardware limitations, rather than software ones. This research examines those limitations and investigates what changes can be made to minimize them. FPGAs (Field Programmable Gate Arrays) prove to be a promising alternative to GPUs (Graphical Processing Units) in both power consumption and interface flexibility, and storage optimization allows for these accelerators to access data more quickly and without the need for a CPU (Central Processing Unit) interface. Further research should develop models for optimizing FPGA integration, and test physical science algorithms on different processor and storage models to find optimal configurations.